id: 05779778
dt: a
an: 05779778
au: Cormode, Graham; Mitzenmacher, Michael; Thaler, Justin
ti: Streaming graph computations with a helpful advisor.
so: de Berg, Mark (ed.) et al., Algorithms ‒ ESA 2010. 18th annual European
    symposium, Liverpool, UK, September 6‒8, 2010. Proceedings, Part I.
    Berlin: Springer (ISBN 978-3-642-15774-5/pbk). Lecture Notes in
    Computer Science 6346, 231-242 (2010).
py: 2010
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-15775-2_20
ab: Summary: Motivated by the trend to outsource work to commercial cloud
    computing services, we consider a variation of the streaming paradigm
    where a streaming algorithm can be assisted by a powerful helper that
    can provide annotations to the data stream. We extend previous work on
    such annotation models by considering a number of graph streaming
    problems. Without annotations, streaming algorithms for graph problems
    generally require significant memory; we show that for many standard
    problems, including all graph problems that can be expressed with
    totally unimodular integer programming formulations, only constant
    memory is needed for single-pass algorithms given linear-sized
    annotations. We also obtain a protocol achieving optimal tradeoffs
    between annotation length and memory usage for matrix-vector
    multiplication; this result contributes to a trend of recent research
    on numerical linear algebra in streaming models.
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